class GenericDetector: def __init__(self, path, headless, buffer_size, codec, fps, net, confidence, frameCount=32, notifier=None, url=None, debugmemory=False, blocking=False): # initialize the video stream, allow the camera sensor to warm up, # and initialize the FPS counter print("[INFO] starting video stream...") self.vs = VideoStream(src=0).start() # vs = VideoStream(usePiCamera=True).start() time.sleep(2.0) self.fps = FPS().start() # initialize the key clip writer and the motionFrames # and consecFrames to track frames without motion self.buffer_size = buffer_size self.kcw = KeyClipWriter(bufSize=buffer_size) self.consecFrames = 0 # number of frames with no motion self.prev_detections = None # initialize the output frame and a lock used to ensure thread-safe # exchanges of the output frames (useful when multiple browsers/tabs # are viewing the stream) self.outputFrame = None self.lock = threading.Lock() self.path = path self.headless = headless self.codec = codec self.fps_rate = fps self.net = net self.confidence = confidence self.frameCount = frameCount self.notifier = notifier self.url = url self.debugmemory = debugmemory self.blocking = blocking if not self.blocking: self.inputQueue = Queue(maxsize=1) self.outputQueue = Queue(maxsize=1) def __del__(self): self.kcw.finish() # stop the timer and display FPS information self.vs.stop() self.fps.stop() print("[INFO] elapsed time: {:.2f}".format(fps.elapsed())) print("[INFO] approx. FPS: {:.2f}".format(fps.fps())) def loop_over_detections(self, frame, detections, w, h): detected = False msg = [] # loop over the detections for i in np.arange(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with # the prediction confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence > self.confidence: # extract the index of the class label from the # `detections`, then compute the (x, y)-coordinates of # the bounding box for the object idx = int(detections[0, 0, i, 1]) box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # draw the timestamp timestamp = datetime.datetime.now() cv2.putText(frame, timestamp.strftime("%Y.%m.%d %H:%M:%S"), (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[0], 2) # draw the prediction on the frame label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100) cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2) y = startY - 15 if startY - 15 > 15 else startY + 15 cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2) # record this frame if self.prev_detections is None: return if np.array_equal(self.prev_detections, detections[0, 0, :, 1]): return detected = True # if we are not already recording, start recording if not self.kcw.recording: difference = set(detections[0, 0, :, 1]).symmetric_difference( set(self.prev_detections)) for o in difference: msg.append(CLASSES[int(o)]) timestamp = datetime.datetime.now() ts = timestamp.strftime("%Y%m%d-%H%M%S") p = "{}/{}.avi".format(self.path, ts) print(timestamp, 'Start recording', p) self.kcw.start(p, cv2.VideoWriter_fourcc(*self.codec), self.fps_rate) if len(msg) > 0: msg = ', '.join(msg) msg = '{}: {} appeared'.format(ts, msg) if self.url: msg = msg + ' {}/video_feed'.format(self.url) print(msg) if self.notifier: self.notifier(msg) return detected def detect_object_in_frame(self, frame): frame = imutils.resize(frame, width=400) # update the key frame clip buffer self.kcw.update(frame) # grab the frame dimensions and convert it to a blob (h, w) = frame.shape[:2] # run detection in current process detections = self.classify_frame(frame) if detections is not None: if self.loop_over_detections(frame, detections, w, h): self.consecFrames = 0 self.prev_detections = detections[ 0, 0, :, 1] # save objects, detected on current frame return frame def _classify_frame(self, frame): frame = cv2.resize(frame, (300, 300)) blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5) # set the blob as input to our deep learning object # detector and obtain the detections self.net.setInput(blob) return self.net.forward() def classify_frame(self, frame): if self.blocking: return self._classify_frame(frame) # if the input queue *is* empty, give the current frame to # classify if self.inputQueue.empty(): self.inputQueue.put(frame) # if the output queue *is not* empty, grab the detections if not self.outputQueue.empty(): return self.outputQueue.get() def loop_classify_frame(self): assert not self.blocking # only for non-blocking case # keep looping while True: # check to see if there is a frame in our input queue if self.inputQueue.empty(): continue # grab the frame from the input queue, resize it, and # construct a blob from it frame = self.inputQueue.get() detections = self._classify_frame(frame) # write the detections to the output queue self.outputQueue.put(detections) def generate(self): "Yield image/jpeg for web serving" # grab global references to the output frame and lock variables # loop over frames from the output stream while True: # wait until the lock is acquired with self.lock: # check if the output frame is available, otherwise skip # the iteration of the loop if self.outputFrame is None: continue # encode the frame in JPEG format (flag, encodedImage) = cv2.imencode(".jpg", self.outputFrame) # ensure the frame was successfully encoded if not flag: continue # yield the output frame in the byte format yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + bytearray(encodedImage) + b'\r\n')
updateConsecFrames = radius <= 10 # only proceed if the redius meets a minimum size if radius > 10: # reset the number of consecutive frames with # *no* action to zero and draw the circle # surrounding the object consecFrames = 0 cv2.circle(frame, (int(x), int(y)), int(radius), (0, 0, 255), 2) # if we are not already recording, start recording if not kcw.recording: timestamp = datetime.datetime.now() p = "{}/{}.avi".format(args["output"], timestamp.strftime("%Y%m%d-%H%M%S")) kcw.start(p, cv2.VideoWriter_fourcc(*args["codec"]), args["fps"]) # otherwise, no action has taken place in this frame, so # increment the number of consecutive frames that contain # no action if updateConsecFrames: consecFrames += 1 # update the key frame clip buffer kcw.update(frame) # if we are recording and reached a threshold on consecutive # number of frames with no action, stop recording the clip if kcw.recording and consecFrames == args["buffer_size"]: kcw.finish()
updateConsecFrames = True cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF if key == ord('q'): #finaliza o monitoramento break if key == ord('x'): #evento que aciona a região de interesse print("Botão Pressionado...efetuando a gravação, aguarde alguns segundos\n") consecFrames=0 if not kcw.recording: timestamp = datetime.datetime.now() p = "{}/{}.avi".format('output', timestamp.strftime("%d-%m-%Y-%H:%M:%S")) kcw.start(p, cv2.VideoWriter_fourcc(*mn.parametros['codec']),mn.parametros['fps']) if updateConsecFrames: consecFrames += 1 kcw.update(frame) if kcw.recording and consecFrames == mn.parametros['buffer']: kcw.finish() if kcw.recording: kcw.finish() cv2.destroyAllWindows() vs.stop()
timestring]) cv2.imwrite(snapPath + ".jpg", fullFrame) # reset the counter between snapshots framesSinceSnap = 0 # start recording if we aren't already if not kcw.recording: # construct the path to the video file videoPath = os.path.sep.join([conf["output_path"], timestring]) # instantiate the video codec object and start the key # clip writer fourcc = cv2.VideoWriter_fourcc(*conf["codec"]) kcw.start("{}.avi".format(videoPath), fourcc, conf["fps"]) # check if no motion was detected in this frame and then increment # the number of consecutive frames without motion if not motionThisFrame: framesWithoutMotion += 1 # update the key frame clip buffer kcw.update(frame) # check to see if the number of frames without motion is above our # defined threshold noMotion = framesWithoutMotion >= conf["keyclipwriter_buffersize"] # stop recording if there is no motion if kcw.recording and noMotion:
#look for pedestrians logging.info("Saw Motion, Checking for Peds at: {}".format(ts)) pedRet = pDet.count_peds(roi) peds = pedRet[0] if peds > 0: pedFrames += 1 logging.info("Found {} Pedestrians, {} times".format(peds, pedFrames)) if pedFrames >= conf["ped_min_detections"]: if not kcw.recording: #p = TempImage(ext= conf["filetype"]) timestamp = datetime.datetime.now() p = "./{}.{}".format(timestamp.strftime("%Y%m%d-%H%M%S"),conf["filetype"]) logging.debug("Path of Temp file = {}".format(p)) kcw.start(p, cv2.VideoWriter_fourcc(*conf["codec"]),conf["fps"]) # motion was not detectec, increment the still counter, clear the motion counter else: consecFrames += 1 motionFrames = 0 #if we are recording and the motion has stopped for long enough #or we've reached the buffer length, stop recording if kcw.recording and consecFrames >= conf["buffer_size"]: logging.debug("Reached max buffer, closing the file") kcw.finish() consecFrames = 0 pedFrames = 0 peds = 0
def detect_motion(outputDirectory): global vs, outputFrame, lock frameCount = 32 md = SingleMotionDetector(accumWeight=0.1) total = 0 recordedFrameCount = 0 kcw = KeyClipWriter(bufSize=beforeAndAfterFrames) lastFileName = None while True: # read the next frame from the video stream, resize it, # convert the frame to grayscale, and blur it frame = vs.read() frame = imutils.resize(frame, width=600) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (7, 7), 0) # grab the current timestamp and draw it on the frame timestamp = datetime.datetime.now() cv2.putText(frame, timestamp.strftime("%A %d %B %Y %I:%M:%S%p"), (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 255, 0), 1) # if the total number of frames has reached a sufficient # number to construct a reasonable background model, then # continue to process the frame if total > frameCount: kcw.update(frame) motion = md.detect(gray) # check to see if motion was found in the frame if motion is not None: # unpack the tuple and draw the box surrounding the # "motion area" on the output frame (thresh, (minX, minY, maxX, maxY)) = motion cv2.rectangle(frame, (minX, minY), (maxX, maxY), (0, 0, 255), 2) if kcw.recording is False: recordedFrameCount = 0 timestamp = datetime.datetime.now() lastFileName = "{}/{}.mp4".format( outputDirectory, timestamp.strftime("%Y%m%d-%H%M%S")) kcw.start(lastFileName, cv2.VideoWriter_fourcc(*"MP4V"), fps) logging.info("Started recording") if kcw.recording is True: recordedFrameCount += 1 if recordedFrameCount > beforeAndAfterFrames: logging.info("Stopped recording") kcw.finish() if lastFileName is not None: ah.sendEvent(lastFileName) # update the background model and increment the total number # of frames read thus far md.update(gray) total += 1 # acquire the lock, set the output frame, and release the # lock with lock: outputFrame = frame.copy()
# only proceed if the redius meets a minimum size if radius > 10: # reset the number of consecutive frames with # *no* action to zero and draw the circle # surrounding the object consecFrames = 0 cv2.circle(frame, (int(x), int(y)), int(radius), (0, 0, 255), 2) # if we are not already recording, start recording if not kcw.recording: timestamp = datetime.datetime.now() p = "{}/{}.avi".format(args["output"], timestamp.strftime("%Y%m%d-%H%M%S")) kcw.start(p, cv2.VideoWriter_fourcc(*args["codec"]), args["fps"]) # otherwise, no action has taken place in this frame, so # increment the number of consecutive frames that contain # no action if updateConsecFrames: consecFrames += 1 # update the key frame clip buffer kcw.update(frame) # if we are recording and reached a threshold on consecutive # number of frames with no action, stop recording the clip if kcw.recording and consecFrames == args["buffer_size"]: kcw.finish()
# only proceed if the redius meets a minimum size if radius > 5: # reset the number of consecutive frames with # *no* action to zero and draw the circle # surrounding the object consecFrames = 0 cv2.circle(frame, (int(x), int(y)), int(radius), (0, 0, 255), 2) pts.appendleft(center) # if we are not already recording, start recording if not kcw.recording: timestamp = datetime.datetime.now() video_name = timestamp.strftime("%Y%m%d-%H%M%S") p = "{}/{}.avi".format(args["output"], video_name) kcw.start(p, cv2.cv.CV_FOURCC(*args["codec"]), args["fps"]) print("I'm recording now!") #START TIMER HERE time_start = time.time() #highlights = [HighlightClass(0, 1)] highlights = [] #highlightsJson = '[{"time":0, "duration":1}, {"time":1, "duration":1}]' #highlightsPython = json.loads(highlightsJson) #highlightsPython.append({"time":3, "duration":1}); #print highlightsPython[2]['time'] # otherwise, no action has taken place in this frame, so # increment the number of consecutive frames that contain # no action if updateConsecFrames: consecFrames += 1